{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 目录\n",
    "1. 两种自定义因子的方法——add_formula&append_df\n",
    "2. 什么是事件？（0/1因子）\n",
    "3. 事件选股效果的可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataview loaded successfully.\n"
     ]
    }
   ],
   "source": [
    "from jaqs_fxdayu.data import DataView \n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "dataview_folder = '../Factor'\n",
    "dv = DataView()\n",
    "dv.load_dataview(dataview_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1、两种自定义因子的方法——add_formula&append_df\n",
    "dataview可以通过两种方式计算/添加自定义因子进行分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 方法1：add_formula 基于dataview里已有的字段,通过表达式定义因子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <th>symbol</th>\n",
       "      <th>000001.SZ</th>\n",
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       "      <th>...</th>\n",
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       "      <th>603699.SH</th>\n",
       "      <th>603799.SH</th>\n",
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       "      <th>603858.SH</th>\n",
       "      <th>603885.SH</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.084892</td>\n",
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       "      <td>-0.111690</td>\n",
       "      <td>-0.102975</td>\n",
       "      <td>-0.052910</td>\n",
       "      <td>-0.040881</td>\n",
       "      <td>-0.116740</td>\n",
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       "      <td>-0.082474</td>\n",
       "      <td>0.048699</td>\n",
       "      <td>-0.091097</td>\n",
       "      <td>-0.111111</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.075426</td>\n",
       "      <td>0.105497</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.091437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20140106</th>\n",
       "      <td>-0.121896</td>\n",
       "      <td>-0.137255</td>\n",
       "      <td>-0.095643</td>\n",
       "      <td>-0.059129</td>\n",
       "      <td>-0.165380</td>\n",
       "      <td>-0.111576</td>\n",
       "      <td>-0.106164</td>\n",
       "      <td>0.011311</td>\n",
       "      <td>-0.098121</td>\n",
       "      <td>-0.134470</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.085575</td>\n",
       "      <td>0.132137</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.123726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20140107</th>\n",
       "      <td>-0.118271</td>\n",
       "      <td>-0.138051</td>\n",
       "      <td>-0.109342</td>\n",
       "      <td>-0.060228</td>\n",
       "      <td>-0.174342</td>\n",
       "      <td>-0.122535</td>\n",
       "      <td>-0.104991</td>\n",
       "      <td>0.039841</td>\n",
       "      <td>-0.095745</td>\n",
       "      <td>-0.139847</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.088020</td>\n",
       "      <td>0.076545</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.118594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20140108</th>\n",
       "      <td>-0.115124</td>\n",
       "      <td>-0.144175</td>\n",
       "      <td>-0.159346</td>\n",
       "      <td>-0.063224</td>\n",
       "      <td>-0.179235</td>\n",
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       "      <td>0.118630</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.127941</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 488 columns</p>\n",
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       "symbol      000001.SZ  000002.SZ  000008.SZ  000009.SZ  000012.SZ  000024.SZ  \\\n",
       "trade_date                                                                     \n",
       "20140102    -0.100735  -0.085812  -0.057592  -0.006342  -0.100442  -0.051708   \n",
       "20140103    -0.111690  -0.102975  -0.052910  -0.040881  -0.116740  -0.078923   \n",
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       "20140108    -0.115124  -0.144175  -0.159346  -0.063224  -0.179235  -0.160665   \n",
       "\n",
       "symbol      000027.SZ  000039.SZ  000046.SZ  000059.SZ    ...      601998.SH  \\\n",
       "trade_date                                                ...                  \n",
       "20140102    -0.068143   0.012426  -0.074534  -0.089580    ...      -0.065375   \n",
       "20140103    -0.082474   0.048699  -0.091097  -0.111111    ...      -0.075426   \n",
       "20140106    -0.106164   0.011311  -0.098121  -0.134470    ...      -0.085575   \n",
       "20140107    -0.104991   0.039841  -0.095745  -0.139847    ...      -0.088020   \n",
       "20140108    -0.093103   0.066347  -0.081023  -0.156604    ...      -0.085575   \n",
       "\n",
       "symbol      603000.SH  603160.SH  603288.SH  603699.SH  603799.SH  603833.SH  \\\n",
       "trade_date                                                                     \n",
       "20140102     0.104574        NaN        NaN        NaN        NaN        NaN   \n",
       "20140103     0.105497        NaN        NaN        NaN        NaN        NaN   \n",
       "20140106     0.132137        NaN        NaN        NaN        NaN        NaN   \n",
       "20140107     0.076545        NaN        NaN        NaN        NaN        NaN   \n",
       "20140108     0.118630        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "symbol      603858.SH  603885.SH  603993.SH  \n",
       "trade_date                                   \n",
       "20140102          NaN        NaN  -0.084892  \n",
       "20140103          NaN        NaN  -0.091437  \n",
       "20140106          NaN        NaN  -0.123726  \n",
       "20140107          NaN        NaN  -0.118594  \n",
       "20140108          NaN        NaN  -0.127941  \n",
       "\n",
       "[5 rows x 488 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 直接返回\n",
    "dv.add_formula(\"momentum\", \"Return(close_adj, 20)\", is_quarterly=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.105497</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.091437</td>\n",
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       "    <tr>\n",
       "      <th>20140106</th>\n",
       "      <td>-0.121896</td>\n",
       "      <td>-0.137255</td>\n",
       "      <td>-0.095643</td>\n",
       "      <td>-0.059129</td>\n",
       "      <td>-0.165380</td>\n",
       "      <td>-0.111576</td>\n",
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       "      <td>0.011311</td>\n",
       "      <td>-0.098121</td>\n",
       "      <td>-0.134470</td>\n",
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       "      <td>0.132137</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.123726</td>\n",
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       "      <th>20140107</th>\n",
       "      <td>-0.118271</td>\n",
       "      <td>-0.138051</td>\n",
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       "      <td>-0.060228</td>\n",
       "      <td>-0.174342</td>\n",
       "      <td>-0.122535</td>\n",
       "      <td>-0.104991</td>\n",
       "      <td>0.039841</td>\n",
       "      <td>-0.095745</td>\n",
       "      <td>-0.139847</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.088020</td>\n",
       "      <td>0.076545</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.118594</td>\n",
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       "      <td>-0.085575</td>\n",
       "      <td>0.118630</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.127941</td>\n",
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       "symbol      000001.SZ  000002.SZ  000008.SZ  000009.SZ  000012.SZ  000024.SZ  \\\n",
       "trade_date                                                                     \n",
       "20140102    -0.100735  -0.085812  -0.057592  -0.006342  -0.100442  -0.051708   \n",
       "20140103    -0.111690  -0.102975  -0.052910  -0.040881  -0.116740  -0.078923   \n",
       "20140106    -0.121896  -0.137255  -0.095643  -0.059129  -0.165380  -0.111576   \n",
       "20140107    -0.118271  -0.138051  -0.109342  -0.060228  -0.174342  -0.122535   \n",
       "20140108    -0.115124  -0.144175  -0.159346  -0.063224  -0.179235  -0.160665   \n",
       "\n",
       "symbol      000027.SZ  000039.SZ  000046.SZ  000059.SZ    ...      601998.SH  \\\n",
       "trade_date                                                ...                  \n",
       "20140102    -0.068143   0.012426  -0.074534  -0.089580    ...      -0.065375   \n",
       "20140103    -0.082474   0.048699  -0.091097  -0.111111    ...      -0.075426   \n",
       "20140106    -0.106164   0.011311  -0.098121  -0.134470    ...      -0.085575   \n",
       "20140107    -0.104991   0.039841  -0.095745  -0.139847    ...      -0.088020   \n",
       "20140108    -0.093103   0.066347  -0.081023  -0.156604    ...      -0.085575   \n",
       "\n",
       "symbol      603000.SH  603160.SH  603288.SH  603699.SH  603799.SH  603833.SH  \\\n",
       "trade_date                                                                     \n",
       "20140102     0.104574        NaN        NaN        NaN        NaN        NaN   \n",
       "20140103     0.105497        NaN        NaN        NaN        NaN        NaN   \n",
       "20140106     0.132137        NaN        NaN        NaN        NaN        NaN   \n",
       "20140107     0.076545        NaN        NaN        NaN        NaN        NaN   \n",
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       "\n",
       "symbol      603858.SH  603885.SH  603993.SH  \n",
       "trade_date                                   \n",
       "20140102          NaN        NaN  -0.084892  \n",
       "20140103          NaN        NaN  -0.091437  \n",
       "20140106          NaN        NaN  -0.123726  \n",
       "20140107          NaN        NaN  -0.118594  \n",
       "20140108          NaN        NaN  -0.127941  \n",
       "\n",
       "[5 rows x 488 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 添加到数据集dv里，则计算结果之后可以反复调用\n",
    "dv.add_formula(\"momentum\", \"Return(close_adj, 20)\", is_quarterly=False, add_data=True)\n",
    "dv.get_ts(\"momentum\").head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 附：add_formula 支持的内置公式查询方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>分类</th>\n",
       "      <th>说明</th>\n",
       "      <th>公式</th>\n",
       "      <th>示例</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>四则运算</td>\n",
       "      <td>加法运算</td>\n",
       "      <td>+</td>\n",
       "      <td>close + open</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>四则运算</td>\n",
       "      <td>减法运算</td>\n",
       "      <td>-</td>\n",
       "      <td>close - open</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>四则运算</td>\n",
       "      <td>乘法运算</td>\n",
       "      <td>*</td>\n",
       "      <td>vwap * volume</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>四则运算</td>\n",
       "      <td>除法运算</td>\n",
       "      <td>/</td>\n",
       "      <td>close / open</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>基本数学函数</td>\n",
       "      <td>符号函数，返回值为{-1, 0, 1}</td>\n",
       "      <td>Sign(x)</td>\n",
       "      <td>Sign(close-open)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>基本数学函数</td>\n",
       "      <td>绝对值函数</td>\n",
       "      <td>Abs(x)</td>\n",
       "      <td>Abs(close-open)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>基本数学函数</td>\n",
       "      <td>自然对数</td>\n",
       "      <td>Log(x)</td>\n",
       "      <td>Log(close/open)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>基本数学函数</td>\n",
       "      <td>对x取负</td>\n",
       "      <td>-x</td>\n",
       "      <td>-close</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>基本数学函数</td>\n",
       "      <td>幂函数</td>\n",
       "      <td>^</td>\n",
       "      <td>close ^ 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>基本数学函数</td>\n",
       "      <td>幂函数x^y</td>\n",
       "      <td>Pow(x,y)</td>\n",
       "      <td>Pow(close,2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>基本数学函数</td>\n",
       "      <td>保持符号的幂函数，等价于Sign(x) * (Abs(x)^e)</td>\n",
       "      <td>SignedPower(x,e)</td>\n",
       "      <td>SignedPower(close-open, 0.5)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>基本数学函数</td>\n",
       "      <td>取余函数</td>\n",
       "      <td>%</td>\n",
       "      <td>oi % 10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>逻辑运算</td>\n",
       "      <td>判断是否相等</td>\n",
       "      <td>==</td>\n",
       "      <td>close == open</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>逻辑运算</td>\n",
       "      <td>判断是否不等</td>\n",
       "      <td>!=</td>\n",
       "      <td>close != open</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>逻辑运算</td>\n",
       "      <td>大于</td>\n",
       "      <td>&gt;</td>\n",
       "      <td>close &gt; open</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>逻辑运算</td>\n",
       "      <td>小于</td>\n",
       "      <td>&lt;</td>\n",
       "      <td>close &lt; open</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>逻辑运算</td>\n",
       "      <td>大于等于</td>\n",
       "      <td>&gt;=</td>\n",
       "      <td>close &gt;= open</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>逻辑运算</td>\n",
       "      <td>小于等于</td>\n",
       "      <td>&lt;=</td>\n",
       "      <td>close &lt;= open</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>逻辑运算</td>\n",
       "      <td>逻辑与</td>\n",
       "      <td>&amp;&amp;</td>\n",
       "      <td>(close &gt; open) &amp;&amp; (close &gt; vwap)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>逻辑运算</td>\n",
       "      <td>逻辑或</td>\n",
       "      <td>||</td>\n",
       "      <td>(close &gt; open) || (close &gt; vwap)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>逻辑运算</td>\n",
       "      <td>逻辑非</td>\n",
       "      <td>!</td>\n",
       "      <td>!(close&gt;open)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>逻辑运算</td>\n",
       "      <td>判断值是否为NaN</td>\n",
       "      <td>IsNan(x)</td>\n",
       "      <td>IsNan(net_profit)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>三角函数</td>\n",
       "      <td>正弦函数</td>\n",
       "      <td>Sin(x)</td>\n",
       "      <td>Sin(close/open)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>三角函数</td>\n",
       "      <td>余弦函数</td>\n",
       "      <td>Cos(x)</td>\n",
       "      <td>Cos(close/open)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>三角函数</td>\n",
       "      <td>正切函数</td>\n",
       "      <td>Tan(x)</td>\n",
       "      <td>Tan(close/open)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>三角函数</td>\n",
       "      <td>开平方函数</td>\n",
       "      <td>Sqrt(x)</td>\n",
       "      <td>Sqrt(close^2 + open^2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>取整函数</td>\n",
       "      <td>向上取整</td>\n",
       "      <td>Ceil(x)</td>\n",
       "      <td>Ceil(high)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>取整函数</td>\n",
       "      <td>向下取整</td>\n",
       "      <td>Floor(x)</td>\n",
       "      <td>Floor(low)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>取整函数</td>\n",
       "      <td>四舍五入</td>\n",
       "      <td>Round(x)</td>\n",
       "      <td>Round（close）</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>选择函数</td>\n",
       "      <td>取 x 和 y 同位置上的较大值组成新的DataFrame返回</td>\n",
       "      <td>Max(x,y)</td>\n",
       "      <td>Max(close, open)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>时间序列函数 - 统计</td>\n",
       "      <td>指标在过去n天的标准差</td>\n",
       "      <td>StdDev(x,n)</td>\n",
       "      <td>StdDev(close/Delay(close,1)-1, 10)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>时间序列函数 - 统计</td>\n",
       "      <td>两个指标在过去n天的协方差</td>\n",
       "      <td>Covariance(x,y,n)</td>\n",
       "      <td>Covariance(close, open, 10)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>时间序列函数 - 统计</td>\n",
       "      <td>两个指标在过去n天的相关系数</td>\n",
       "      <td>Correlation(x,y,n)</td>\n",
       "      <td>Correlation(close,open, 10)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>时间序列函数 - 统计</td>\n",
       "      <td>计算指标在过去n天的最小值</td>\n",
       "      <td>Ts_Min(x，n)</td>\n",
       "      <td>Ts_Min(close，5)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>时间序列函数 - 统计</td>\n",
       "      <td>计算指标在过去n天的最大值</td>\n",
       "      <td>Ts_Max(x，n)</td>\n",
       "      <td>Ts_Max(close，5)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>时间序列函数 - 统计</td>\n",
       "      <td>计算指标在过去n天的偏度</td>\n",
       "      <td>Ts_Skewness(x，n)</td>\n",
       "      <td>Ts_Skewness(close，20)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>时间序列函数 - 统计</td>\n",
       "      <td>计算指标在过去n天的峰度</td>\n",
       "      <td>Ts_Kurtosis(x，n)</td>\n",
       "      <td>Ts_Kurtosis(close，20)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>时间序列函数 - 排名</td>\n",
       "      <td>计算指标在过去n天的排名，返回值为名次</td>\n",
       "      <td>Ts_Rank(x, n)</td>\n",
       "      <td>Ts_Rank(close, 5)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>时间序列函数 - 排名</td>\n",
       "      <td>计算指标在过去n天的百分比，返回值为[0.0, 1.0]</td>\n",
       "      <td>Ts_Percentile(x, n)</td>\n",
       "      <td>Ts_Percentile(close, 5)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>时间序列函数 - 排名</td>\n",
       "      <td>计算指标在过去n天所属的quantile，返回值为表示quantile的整数</td>\n",
       "      <td>Ts_Quantile(x, n)</td>\n",
       "      <td>Ts_Quantile(close, 5)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>时间序列函数 - 排名</td>\n",
       "      <td>指数移动平均，以halflife的衰减对x进行指数移动平均</td>\n",
       "      <td>Ewma(x, halflife)</td>\n",
       "      <td>Ewma(x, 3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>横截面函数 - 排名</td>\n",
       "      <td>将指标值在横截面方向排名，返回值为名次</td>\n",
       "      <td>Rank(x)</td>\n",
       "      <td>Rank( close/Delay(close,1)-1 ) 表示按日收益率进行排名</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>横截面函数 - 排名</td>\n",
       "      <td>按分组数据g在每组内将指标值在横截面方向排名，返回值为名次</td>\n",
       "      <td>GroupRank(x,g)</td>\n",
       "      <td>GroupRank(close/Delay(close,1)-1, g) 表示按分组g根据日...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>横截面函数 - 排名</td>\n",
       "      <td>将指标值在横截面方向排名，返回值为排名百分比</td>\n",
       "      <td>Percentile(x)</td>\n",
       "      <td>Percentile(close)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>横截面函数 - 排名</td>\n",
       "      <td>按分组数据g在每组内将指标值在横截面方向排名，返回值为排名百分比</td>\n",
       "      <td>GroupPercentile(x, g, n)</td>\n",
       "      <td>GroupPercentile(close, sw1) 按申万1级行业</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>横截面函数 - 排名</td>\n",
       "      <td>和Rank函数相同，但只有 cond 中值为True的标的参与排名</td>\n",
       "      <td>ConditionRank(x, cond)</td>\n",
       "      <td>GroupRank(close/Delay(close,1)-1, cond) 表示按条件c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>横截面函数 - 排名</td>\n",
       "      <td>根据指标值在横截面方向将标的分成n个quantile，返回值为所属quantile</td>\n",
       "      <td>Quantile(x, n)</td>\n",
       "      <td>Quantile( close/Delay(close,1)-1,5)表示按日收益率分为5档</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>横截面函数 - 排名</td>\n",
       "      <td>按分组数据g在每组内根据指标值在横截面方向将标的分成n个quantile，返回值为所属qua...</td>\n",
       "      <td>GroupQuantile(x, g, n)</td>\n",
       "      <td>GroupQuantile(close/Delay(close,1)-1,g,5) 表示按日...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>横截面函数 - 数据处理</td>\n",
       "      <td>将指标标准化，即在横截面上减去平均值后再除以标准差</td>\n",
       "      <td>Standardize(x)</td>\n",
       "      <td>Standardize(close/Delay(close,1)-1) 表示日收益率的标准化</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>横截面函数 - 数据处理</td>\n",
       "      <td>将指标横截面上去极值，用MAD (Maximum Absolute Deviation)方法...</td>\n",
       "      <td>Cutoff(x, z_score)</td>\n",
       "      <td>Cutoff(close,3) 表示去掉z_score大于3的极值</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>财报函数</td>\n",
       "      <td>将累计财务数据转换为单季财务数据</td>\n",
       "      <td>CumToSingle(x)</td>\n",
       "      <td>CumToSingle(net_profit)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>财报函数</td>\n",
       "      <td>从累计财务数据计算TTM的财务数据</td>\n",
       "      <td>TTM(x)</td>\n",
       "      <td>TTM(net_profit)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>其他</td>\n",
       "      <td>过去 n 天的指数衰减函数，其中 f 是平滑因子。这里 f 是平滑因子，可以赋一个小于 1 ...</td>\n",
       "      <td>Decay_exp(x,f,n)</td>\n",
       "      <td>Decay_exp(close,0.9,10)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>其他</td>\n",
       "      <td>过去n天的线性衰减函数。Decay_linear(x, n) = (x[date] * n ...</td>\n",
       "      <td>Decay_linear(x,n)</td>\n",
       "      <td>Decay_linear(close,15)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>其他</td>\n",
       "      <td>如果 x 的值介于 lower 和 upper，则将其设定为 newval</td>\n",
       "      <td>Tail(x, lower, upper, newval)</td>\n",
       "      <td>Tail(close/open, 0.99, 1.01, 1.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>其他</td>\n",
       "      <td>Step(n) 为每个标的创建一个向量，向量中 n 代表最新日期，n-1 代表前一天，以此类推。</td>\n",
       "      <td>Step(n)</td>\n",
       "      <td>Step(30)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>其他</td>\n",
       "      <td>时间序列函数，计算 x 中的值在过去 n 天中为 nan （非数字）的次数</td>\n",
       "      <td>CountNans(x,n)</td>\n",
       "      <td>CountNans((close-open)^0.5, 10) 表示过去10天内有几天clo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>时间序列函数 - 统计</td>\n",
       "      <td>计算指标在过去n天最大值的坐标</td>\n",
       "      <td>Ts_Argmax(x,n)</td>\n",
       "      <td>Ts_Argmax(high,10)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>时间序列函数 - 统计</td>\n",
       "      <td>计算指标在过去n天最小值的坐标</td>\n",
       "      <td>Ts_Argmin(x,n)</td>\n",
       "      <td>Ts_Argmin(low,10)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>技术指标</td>\n",
       "      <td>根据talib技术指标库计算x中每只股票的技术指标</td>\n",
       "      <td>Ta(ta_method,ta_column,open,high,low,close,vol...</td>\n",
       "      <td>Ta('MACD','macdsignal',open,high,low,close,vol...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              分类                                                 说明  \\\n",
       "0           四则运算                                               加法运算   \n",
       "1           四则运算                                               减法运算   \n",
       "2           四则运算                                               乘法运算   \n",
       "3           四则运算                                               除法运算   \n",
       "4         基本数学函数                                符号函数，返回值为{-1, 0, 1}   \n",
       "5         基本数学函数                                              绝对值函数   \n",
       "6         基本数学函数                                               自然对数   \n",
       "7         基本数学函数                                               对x取负   \n",
       "8         基本数学函数                                                幂函数   \n",
       "9         基本数学函数                                             幂函数x^y   \n",
       "10        基本数学函数                   保持符号的幂函数，等价于Sign(x) * (Abs(x)^e)   \n",
       "11        基本数学函数                                               取余函数   \n",
       "12          逻辑运算                                             判断是否相等   \n",
       "13          逻辑运算                                             判断是否不等   \n",
       "14          逻辑运算                                                 大于   \n",
       "15          逻辑运算                                                 小于   \n",
       "16          逻辑运算                                               大于等于   \n",
       "17          逻辑运算                                               小于等于   \n",
       "18          逻辑运算                                                逻辑与   \n",
       "19          逻辑运算                                                逻辑或   \n",
       "20          逻辑运算                                                逻辑非   \n",
       "21          逻辑运算                                          判断值是否为NaN   \n",
       "22          三角函数                                               正弦函数   \n",
       "23          三角函数                                               余弦函数   \n",
       "24          三角函数                                               正切函数   \n",
       "25          三角函数                                              开平方函数   \n",
       "26          取整函数                                               向上取整   \n",
       "27          取整函数                                               向下取整   \n",
       "28          取整函数                                               四舍五入   \n",
       "29          选择函数                    取 x 和 y 同位置上的较大值组成新的DataFrame返回   \n",
       "..           ...                                                ...   \n",
       "38   时间序列函数 - 统计                                        指标在过去n天的标准差   \n",
       "39   时间序列函数 - 统计                                      两个指标在过去n天的协方差   \n",
       "40   时间序列函数 - 统计                                     两个指标在过去n天的相关系数   \n",
       "41   时间序列函数 - 统计                                      计算指标在过去n天的最小值   \n",
       "42   时间序列函数 - 统计                                      计算指标在过去n天的最大值   \n",
       "43   时间序列函数 - 统计                                       计算指标在过去n天的偏度   \n",
       "44   时间序列函数 - 统计                                       计算指标在过去n天的峰度   \n",
       "45   时间序列函数 - 排名                                计算指标在过去n天的排名，返回值为名次   \n",
       "46   时间序列函数 - 排名                       计算指标在过去n天的百分比，返回值为[0.0, 1.0]   \n",
       "47   时间序列函数 - 排名             计算指标在过去n天所属的quantile，返回值为表示quantile的整数   \n",
       "48   时间序列函数 - 排名                      指数移动平均，以halflife的衰减对x进行指数移动平均   \n",
       "49    横截面函数 - 排名                                将指标值在横截面方向排名，返回值为名次   \n",
       "50    横截面函数 - 排名                      按分组数据g在每组内将指标值在横截面方向排名，返回值为名次   \n",
       "51    横截面函数 - 排名                             将指标值在横截面方向排名，返回值为排名百分比   \n",
       "52    横截面函数 - 排名                   按分组数据g在每组内将指标值在横截面方向排名，返回值为排名百分比   \n",
       "53    横截面函数 - 排名                  和Rank函数相同，但只有 cond 中值为True的标的参与排名   \n",
       "54    横截面函数 - 排名          根据指标值在横截面方向将标的分成n个quantile，返回值为所属quantile   \n",
       "55    横截面函数 - 排名  按分组数据g在每组内根据指标值在横截面方向将标的分成n个quantile，返回值为所属qua...   \n",
       "56  横截面函数 - 数据处理                          将指标标准化，即在横截面上减去平均值后再除以标准差   \n",
       "57  横截面函数 - 数据处理  将指标横截面上去极值，用MAD (Maximum Absolute Deviation)方法...   \n",
       "58          财报函数                                   将累计财务数据转换为单季财务数据   \n",
       "59          财报函数                                  从累计财务数据计算TTM的财务数据   \n",
       "60            其他  过去 n 天的指数衰减函数，其中 f 是平滑因子。这里 f 是平滑因子，可以赋一个小于 1 ...   \n",
       "61            其他  过去n天的线性衰减函数。Decay_linear(x, n) = (x[date] * n ...   \n",
       "62            其他              如果 x 的值介于 lower 和 upper，则将其设定为 newval   \n",
       "63            其他   Step(n) 为每个标的创建一个向量，向量中 n 代表最新日期，n-1 代表前一天，以此类推。   \n",
       "64            其他              时间序列函数，计算 x 中的值在过去 n 天中为 nan （非数字）的次数   \n",
       "65   时间序列函数 - 统计                                    计算指标在过去n天最大值的坐标   \n",
       "66   时间序列函数 - 统计                                    计算指标在过去n天最小值的坐标   \n",
       "67          技术指标                          根据talib技术指标库计算x中每只股票的技术指标   \n",
       "\n",
       "                                                   公式  \\\n",
       "0                                                   +   \n",
       "1                                                   -   \n",
       "2                                                   *   \n",
       "3                                                   /   \n",
       "4                                             Sign(x)   \n",
       "5                                              Abs(x)   \n",
       "6                                              Log(x)   \n",
       "7                                                  -x   \n",
       "8                                                   ^   \n",
       "9                                            Pow(x,y)   \n",
       "10                                   SignedPower(x,e)   \n",
       "11                                                  %   \n",
       "12                                                 ==   \n",
       "13                                                 !=   \n",
       "14                                                  >   \n",
       "15                                                  <   \n",
       "16                                                 >=   \n",
       "17                                                 <=   \n",
       "18                                                 &&   \n",
       "19                                                 ||   \n",
       "20                                                  !   \n",
       "21                                           IsNan(x)   \n",
       "22                                             Sin(x)   \n",
       "23                                             Cos(x)   \n",
       "24                                             Tan(x)   \n",
       "25                                            Sqrt(x)   \n",
       "26                                            Ceil(x)   \n",
       "27                                           Floor(x)   \n",
       "28                                           Round(x)   \n",
       "29                                           Max(x,y)   \n",
       "..                                                ...   \n",
       "38                                        StdDev(x,n)   \n",
       "39                                  Covariance(x,y,n)   \n",
       "40                                 Correlation(x,y,n)   \n",
       "41                                        Ts_Min(x，n)   \n",
       "42                                        Ts_Max(x，n)   \n",
       "43                                   Ts_Skewness(x，n)   \n",
       "44                                   Ts_Kurtosis(x，n)   \n",
       "45                                      Ts_Rank(x, n)   \n",
       "46                                Ts_Percentile(x, n)   \n",
       "47                                  Ts_Quantile(x, n)   \n",
       "48                                  Ewma(x, halflife)   \n",
       "49                                            Rank(x)   \n",
       "50                                     GroupRank(x,g)   \n",
       "51                                      Percentile(x)   \n",
       "52                           GroupPercentile(x, g, n)   \n",
       "53                             ConditionRank(x, cond)   \n",
       "54                                     Quantile(x, n)   \n",
       "55                             GroupQuantile(x, g, n)   \n",
       "56                                     Standardize(x)   \n",
       "57                                 Cutoff(x, z_score)   \n",
       "58                                     CumToSingle(x)   \n",
       "59                                             TTM(x)   \n",
       "60                                   Decay_exp(x,f,n)   \n",
       "61                                  Decay_linear(x,n)   \n",
       "62                      Tail(x, lower, upper, newval)   \n",
       "63                                            Step(n)   \n",
       "64                                     CountNans(x,n)   \n",
       "65                                     Ts_Argmax(x,n)   \n",
       "66                                     Ts_Argmin(x,n)   \n",
       "67  Ta(ta_method,ta_column,open,high,low,close,vol...   \n",
       "\n",
       "                                                   示例  \n",
       "0                                        close + open  \n",
       "1                                        close - open  \n",
       "2                                       vwap * volume  \n",
       "3                                        close / open  \n",
       "4                                    Sign(close-open)  \n",
       "5                                     Abs(close-open)  \n",
       "6                                     Log(close/open)  \n",
       "7                                              -close  \n",
       "8                                           close ^ 2  \n",
       "9                                        Pow(close,2)  \n",
       "10                       SignedPower(close-open, 0.5)  \n",
       "11                                            oi % 10  \n",
       "12                                      close == open  \n",
       "13                                      close != open  \n",
       "14                                       close > open  \n",
       "15                                       close < open  \n",
       "16                                      close >= open  \n",
       "17                                      close <= open  \n",
       "18                   (close > open) && (close > vwap)  \n",
       "19                   (close > open) || (close > vwap)  \n",
       "20                                      !(close>open)  \n",
       "21                                  IsNan(net_profit)  \n",
       "22                                    Sin(close/open)  \n",
       "23                                    Cos(close/open)  \n",
       "24                                    Tan(close/open)  \n",
       "25                             Sqrt(close^2 + open^2)  \n",
       "26                                         Ceil(high)  \n",
       "27                                         Floor(low)  \n",
       "28                                       Round（close）  \n",
       "29                                   Max(close, open)  \n",
       "..                                                ...  \n",
       "38                 StdDev(close/Delay(close,1)-1, 10)  \n",
       "39                        Covariance(close, open, 10)  \n",
       "40                        Correlation(close,open, 10)  \n",
       "41                                    Ts_Min(close，5)  \n",
       "42                                    Ts_Max(close，5)  \n",
       "43                              Ts_Skewness(close，20)  \n",
       "44                              Ts_Kurtosis(close，20)  \n",
       "45                                  Ts_Rank(close, 5)  \n",
       "46                            Ts_Percentile(close, 5)  \n",
       "47                              Ts_Quantile(close, 5)  \n",
       "48                                         Ewma(x, 3)  \n",
       "49         Rank( close/Delay(close,1)-1 ) 表示按日收益率进行排名  \n",
       "50  GroupRank(close/Delay(close,1)-1, g) 表示按分组g根据日...  \n",
       "51                                  Percentile(close)  \n",
       "52                GroupPercentile(close, sw1) 按申万1级行业  \n",
       "53  GroupRank(close/Delay(close,1)-1, cond) 表示按条件c...  \n",
       "54     Quantile( close/Delay(close,1)-1,5)表示按日收益率分为5档  \n",
       "55  GroupQuantile(close/Delay(close,1)-1,g,5) 表示按日...  \n",
       "56     Standardize(close/Delay(close,1)-1) 表示日收益率的标准化  \n",
       "57                  Cutoff(close,3) 表示去掉z_score大于3的极值  \n",
       "58                            CumToSingle(net_profit)  \n",
       "59                                    TTM(net_profit)  \n",
       "60                            Decay_exp(close,0.9,10)  \n",
       "61                             Decay_linear(close,15)  \n",
       "62                  Tail(close/open, 0.99, 1.01, 1.0)  \n",
       "63                                           Step(30)  \n",
       "64  CountNans((close-open)^0.5, 10) 表示过去10天内有几天clo...  \n",
       "65                                 Ts_Argmax(high,10)  \n",
       "66                                  Ts_Argmin(low,10)  \n",
       "67  Ta('MACD','macdsignal',open,high,low,close,vol...  \n",
       "\n",
       "[68 rows x 4 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 完整文档\n",
    "dv.func_doc.doc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['+', '-', '*', '/', 'Sign(x)', 'Abs(x)', 'Log(x)', '-x', '^',\n",
       "       'Pow(x,y)', 'SignedPower(x,e)', '%', '==', '!=', '>', '<', '>=',\n",
       "       '<=', '&&', '||', '!', 'IsNan(x)', 'Sin(x)', 'Cos(x)', 'Tan(x)',\n",
       "       'Sqrt(x)', 'Ceil(x)', 'Floor(x)', 'Round(x)', 'Max(x,y)',\n",
       "       'Min(x,y)', 'If(cond,x,y)', 'Delay(x,n)', 'Ts_Sum(x,n)',\n",
       "       'Ts_Product(x,n)', 'Delta(x,n)', 'Return(x,n,log)', 'Ts_Mean(x，n)',\n",
       "       'StdDev(x,n)', 'Covariance(x,y,n)', 'Correlation(x,y,n)',\n",
       "       'Ts_Min(x，n)', 'Ts_Max(x，n)', 'Ts_Skewness(x，n)',\n",
       "       'Ts_Kurtosis(x，n)', 'Ts_Rank(x, n)', 'Ts_Percentile(x, n)',\n",
       "       'Ts_Quantile(x, n)', 'Ewma(x, halflife)', 'Rank(x)',\n",
       "       'GroupRank(x,g)', 'Percentile(x)', 'GroupPercentile(x, g, n)',\n",
       "       'ConditionRank(x, cond)', 'Quantile(x, n)',\n",
       "       'GroupQuantile(x, g, n)', 'Standardize(x)', 'Cutoff(x, z_score)',\n",
       "       'CumToSingle(x)', 'TTM(x)', 'Decay_exp(x,f,n)',\n",
       "       'Decay_linear(x,n)', 'Tail(x, lower, upper, newval)', 'Step(n)',\n",
       "       'CountNans(x,n)', 'Ts_Argmax(x,n)', 'Ts_Argmin(x,n)',\n",
       "       'Ta(ta_method,ta_column,open,high,low,close,volume,*args)'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 函数一览\n",
    "dv.func_doc.funcs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['四则运算', '基本数学函数', '逻辑运算', '三角函数', '取整函数', '选择函数',\n",
       "       '时间序列函数 - 基本数学运算', '时间序列函数 - 统计', '时间序列函数 - 排名', '横截面函数 - 排名',\n",
       "       '横截面函数 - 数据处理', '财报函数', '其他', '技术指标'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 函数类型\n",
    "dv.func_doc.types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['加法运算', '减法运算', '乘法运算', '除法运算', '符号函数，返回值为{-1, 0, 1}', '绝对值函数',\n",
       "       '自然对数', '对x取负', '幂函数', '幂函数x^y',\n",
       "       '保持符号的幂函数，等价于Sign(x) * (Abs(x)^e)', '取余函数', '判断是否相等', '判断是否不等',\n",
       "       '大于', '小于', '大于等于', '小于等于', '逻辑与', '逻辑或', '逻辑非', '判断值是否为NaN',\n",
       "       '正弦函数', '余弦函数', '正切函数', '开平方函数', '向上取整', '向下取整', '四舍五入',\n",
       "       '取 x 和 y 同位置上的较大值组成新的DataFrame返回',\n",
       "       '取 x 和 y 同位置上的较小值组成新的DataFrame返回', 'cond为True取x的值，反之取y的值',\n",
       "       '指标n个周期前的值', '指标在过去n天的和', '指标在过去 n 天的积', '指标当前值与n天前的值的差',\n",
       "       '计算指标相比n天前的变化率，默认计算百分比变化率；当log为1时，计算对数变化率;为0时计算普通变化率',\n",
       "       '计算指标在过去n天的平均值', '指标在过去n天的标准差', '两个指标在过去n天的协方差', '两个指标在过去n天的相关系数',\n",
       "       '计算指标在过去n天的最小值', '计算指标在过去n天的最大值', '计算指标在过去n天的偏度', '计算指标在过去n天的峰度',\n",
       "       '计算指标在过去n天的排名，返回值为名次', '计算指标在过去n天的百分比，返回值为[0.0, 1.0]',\n",
       "       '计算指标在过去n天所属的quantile，返回值为表示quantile的整数',\n",
       "       '指数移动平均，以halflife的衰减对x进行指数移动平均', '将指标值在横截面方向排名，返回值为名次',\n",
       "       '按分组数据g在每组内将指标值在横截面方向排名，返回值为名次', '将指标值在横截面方向排名，返回值为排名百分比',\n",
       "       '按分组数据g在每组内将指标值在横截面方向排名，返回值为排名百分比',\n",
       "       '和Rank函数相同，但只有 cond 中值为True的标的参与排名',\n",
       "       '根据指标值在横截面方向将标的分成n个quantile，返回值为所属quantile',\n",
       "       '按分组数据g在每组内根据指标值在横截面方向将标的分成n个quantile，返回值为所属quantile',\n",
       "       '将指标标准化，即在横截面上减去平均值后再除以标准差',\n",
       "       '将指标横截面上去极值，用MAD (Maximum Absolute Deviation)方法, z_score为极值判断标准',\n",
       "       '将累计财务数据转换为单季财务数据', '从累计财务数据计算TTM的财务数据',\n",
       "       '过去 n 天的指数衰减函数，其中 f 是平滑因子。这里 f 是平滑因子，可以赋一个小于 1 的值。Decay_exp(x, f, n) = (x[date] + x[date - 1] * f + … +x[date – n - 1] * (f (n – 1))) / (1 + f + … + f ^ (n - 1))',\n",
       "       '过去n天的线性衰减函数。Decay_linear(x, n) = (x[date] * n + x[date - 1] * (n - 1) + … + x[date – n - 1]) / (n + (n - 1) + … + 1)',\n",
       "       '如果 x 的值介于 lower 和 upper，则将其设定为 newval',\n",
       "       'Step(n) 为每个标的创建一个向量，向量中 n 代表最新日期，n-1 代表前一天，以此类推。',\n",
       "       '时间序列函数，计算 x 中的值在过去 n 天中为 nan （非数字）的次数', '计算指标在过去n天最大值的坐标',\n",
       "       '计算指标在过去n天最小值的坐标', '根据talib技术指标库计算x中每只股票的技术指标'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 函数描述\n",
    "dv.func_doc.descriptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>分类</th>\n",
       "      <th>说明</th>\n",
       "      <th>公式</th>\n",
       "      <th>示例</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>基本数学函数</td>\n",
       "      <td>符号函数，返回值为{-1, 0, 1}</td>\n",
       "      <td>Sign(x)</td>\n",
       "      <td>Sign(close-open)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>基本数学函数</td>\n",
       "      <td>绝对值函数</td>\n",
       "      <td>Abs(x)</td>\n",
       "      <td>Abs(close-open)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>基本数学函数</td>\n",
       "      <td>自然对数</td>\n",
       "      <td>Log(x)</td>\n",
       "      <td>Log(close/open)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>基本数学函数</td>\n",
       "      <td>对x取负</td>\n",
       "      <td>-x</td>\n",
       "      <td>-close</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>基本数学函数</td>\n",
       "      <td>幂函数</td>\n",
       "      <td>^</td>\n",
       "      <td>close ^ 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>基本数学函数</td>\n",
       "      <td>幂函数x^y</td>\n",
       "      <td>Pow(x,y)</td>\n",
       "      <td>Pow(close,2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>基本数学函数</td>\n",
       "      <td>保持符号的幂函数，等价于Sign(x) * (Abs(x)^e)</td>\n",
       "      <td>SignedPower(x,e)</td>\n",
       "      <td>SignedPower(close-open, 0.5)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>基本数学函数</td>\n",
       "      <td>取余函数</td>\n",
       "      <td>%</td>\n",
       "      <td>oi % 10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        分类                                说明                公式  \\\n",
       "4   基本数学函数               符号函数，返回值为{-1, 0, 1}           Sign(x)   \n",
       "5   基本数学函数                             绝对值函数            Abs(x)   \n",
       "6   基本数学函数                              自然对数            Log(x)   \n",
       "7   基本数学函数                              对x取负                -x   \n",
       "8   基本数学函数                               幂函数                 ^   \n",
       "9   基本数学函数                            幂函数x^y          Pow(x,y)   \n",
       "10  基本数学函数  保持符号的幂函数，等价于Sign(x) * (Abs(x)^e)  SignedPower(x,e)   \n",
       "11  基本数学函数                              取余函数                 %   \n",
       "\n",
       "                              示例  \n",
       "4               Sign(close-open)  \n",
       "5                Abs(close-open)  \n",
       "6                Log(close/open)  \n",
       "7                         -close  \n",
       "8                      close ^ 2  \n",
       "9                   Pow(close,2)  \n",
       "10  SignedPower(close-open, 0.5)  \n",
       "11                       oi % 10  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 根据函数类型查询该类型下所有的函数\n",
    "dv.func_doc.search_by_type(\"数学函数\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>分类</th>\n",
       "      <th>说明</th>\n",
       "      <th>公式</th>\n",
       "      <th>示例</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>基本数学函数</td>\n",
       "      <td>绝对值函数</td>\n",
       "      <td>Abs(x)</td>\n",
       "      <td>Abs(close-open)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       分类     说明      公式               示例\n",
       "5  基本数学函数  绝对值函数  Abs(x)  Abs(close-open)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 根据函数描述查询可能符合该描述的所有的函数\n",
    "dv.func_doc.search_by_description(\"绝对值\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>分类</th>\n",
       "      <th>说明</th>\n",
       "      <th>公式</th>\n",
       "      <th>示例</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>三角函数</td>\n",
       "      <td>正切函数</td>\n",
       "      <td>Tan(x)</td>\n",
       "      <td>Tan(close/open)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      分类    说明      公式               示例\n",
       "24  三角函数  正切函数  Tan(x)  Tan(close/open)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 根据函数名查询该函数\n",
    "dv.func_doc.search_by_func(\"Tan\",precise=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>分类</th>\n",
       "      <th>说明</th>\n",
       "      <th>公式</th>\n",
       "      <th>示例</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>三角函数</td>\n",
       "      <td>正切函数</td>\n",
       "      <td>Tan(x)</td>\n",
       "      <td>Tan(close/open)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>横截面函数 - 数据处理</td>\n",
       "      <td>将指标标准化，即在横截面上减去平均值后再除以标准差</td>\n",
       "      <td>Standardize(x)</td>\n",
       "      <td>Standardize(close/Delay(close,1)-1) 表示日收益率的标准化</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              分类                         说明              公式  \\\n",
       "24          三角函数                       正切函数          Tan(x)   \n",
       "56  横截面函数 - 数据处理  将指标标准化，即在横截面上减去平均值后再除以标准差  Standardize(x)   \n",
       "\n",
       "                                                示例  \n",
       "24                                 Tan(close/open)  \n",
       "56  Standardize(close/Delay(close,1)-1) 表示日收益率的标准化  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 根据函数名查询该函数 -模糊查询\n",
    "dv.func_doc.search_by_func(\"Tan\",precise=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 更高自由度下的自定义因子\n",
    "内置的函数终究有限，add_formula可以通过事先定义并注册一些因子计算中需要的函数方法，完成更高自由度的因子计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 定义指数平均计算函数-传入一个时间为索引,股票为columns的Dataframe,计算其指数平均序列\n",
    "# SMAtoday=m/n * Pricetoday + ( n-m )/n * SMAyesterday;\n",
    "def sma(df, n, m):\n",
    "    a = n / m - 1\n",
    "    r = df.ewm(com=a, axis=0, adjust=False)\n",
    "    return r.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>symbol</th>\n",
       "      <th>000001.SZ</th>\n",
       "      <th>000002.SZ</th>\n",
       "      <th>000008.SZ</th>\n",
       "      <th>000009.SZ</th>\n",
       "      <th>000012.SZ</th>\n",
       "      <th>000024.SZ</th>\n",
       "      <th>000027.SZ</th>\n",
       "      <th>000039.SZ</th>\n",
       "      <th>000046.SZ</th>\n",
       "      <th>000059.SZ</th>\n",
       "      <th>...</th>\n",
       "      <th>601998.SH</th>\n",
       "      <th>603000.SH</th>\n",
       "      <th>603160.SH</th>\n",
       "      <th>603288.SH</th>\n",
       "      <th>603699.SH</th>\n",
       "      <th>603799.SH</th>\n",
       "      <th>603833.SH</th>\n",
       "      <th>603858.SH</th>\n",
       "      <th>603885.SH</th>\n",
       "      <th>603993.SH</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20140102</th>\n",
       "      <td>670.001224</td>\n",
       "      <td>866.321144</td>\n",
       "      <td>55.978992</td>\n",
       "      <td>56.156775</td>\n",
       "      <td>155.501418</td>\n",
       "      <td>96.905673</td>\n",
       "      <td>45.642678</td>\n",
       "      <td>333.108222</td>\n",
       "      <td>131.600373</td>\n",
       "      <td>7.665619</td>\n",
       "      <td>...</td>\n",
       "      <td>4.559056</td>\n",
       "      <td>75.925974</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.630090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20140103</th>\n",
       "      <td>670.754419</td>\n",
       "      <td>864.895402</td>\n",
       "      <td>55.622882</td>\n",
       "      <td>56.221643</td>\n",
       "      <td>155.191833</td>\n",
       "      <td>96.852825</td>\n",
       "      <td>45.563959</td>\n",
       "      <td>336.905794</td>\n",
       "      <td>131.232323</td>\n",
       "      <td>7.624630</td>\n",
       "      <td>...</td>\n",
       "      <td>4.561700</td>\n",
       "      <td>76.535824</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.604232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20140106</th>\n",
       "      <td>669.232846</td>\n",
       "      <td>858.583814</td>\n",
       "      <td>55.029013</td>\n",
       "      <td>56.140789</td>\n",
       "      <td>153.899560</td>\n",
       "      <td>96.151771</td>\n",
       "      <td>45.326884</td>\n",
       "      <td>339.232586</td>\n",
       "      <td>130.538272</td>\n",
       "      <td>7.537741</td>\n",
       "      <td>...</td>\n",
       "      <td>4.553090</td>\n",
       "      <td>76.943164</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.546882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20140107</th>\n",
       "      <td>666.620239</td>\n",
       "      <td>850.197604</td>\n",
       "      <td>54.314453</td>\n",
       "      <td>55.990621</td>\n",
       "      <td>152.229109</td>\n",
       "      <td>95.018897</td>\n",
       "      <td>45.027148</td>\n",
       "      <td>341.799375</td>\n",
       "      <td>129.548772</td>\n",
       "      <td>7.426478</td>\n",
       "      <td>...</td>\n",
       "      <td>4.539106</td>\n",
       "      <td>77.435346</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.481907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20140108</th>\n",
       "      <td>664.622559</td>\n",
       "      <td>841.700257</td>\n",
       "      <td>53.517639</td>\n",
       "      <td>55.718069</td>\n",
       "      <td>150.533678</td>\n",
       "      <td>93.550863</td>\n",
       "      <td>44.788736</td>\n",
       "      <td>344.012293</td>\n",
       "      <td>128.733057</td>\n",
       "      <td>7.313338</td>\n",
       "      <td>...</td>\n",
       "      <td>4.525617</td>\n",
       "      <td>78.146377</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.410478</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 488 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "symbol       000001.SZ   000002.SZ  000008.SZ  000009.SZ   000012.SZ  \\\n",
       "trade_date                                                             \n",
       "20140102    670.001224  866.321144  55.978992  56.156775  155.501418   \n",
       "20140103    670.754419  864.895402  55.622882  56.221643  155.191833   \n",
       "20140106    669.232846  858.583814  55.029013  56.140789  153.899560   \n",
       "20140107    666.620239  850.197604  54.314453  55.990621  152.229109   \n",
       "20140108    664.622559  841.700257  53.517639  55.718069  150.533678   \n",
       "\n",
       "symbol      000024.SZ  000027.SZ   000039.SZ   000046.SZ  000059.SZ  \\\n",
       "trade_date                                                            \n",
       "20140102    96.905673  45.642678  333.108222  131.600373   7.665619   \n",
       "20140103    96.852825  45.563959  336.905794  131.232323   7.624630   \n",
       "20140106    96.151771  45.326884  339.232586  130.538272   7.537741   \n",
       "20140107    95.018897  45.027148  341.799375  129.548772   7.426478   \n",
       "20140108    93.550863  44.788736  344.012293  128.733057   7.313338   \n",
       "\n",
       "symbol        ...      601998.SH  603000.SH  603160.SH  603288.SH  603699.SH  \\\n",
       "trade_date    ...                                                              \n",
       "20140102      ...       4.559056  75.925974        NaN        NaN        NaN   \n",
       "20140103      ...       4.561700  76.535824        NaN        NaN        NaN   \n",
       "20140106      ...       4.553090  76.943164        NaN        NaN        NaN   \n",
       "20140107      ...       4.539106  77.435346        NaN        NaN        NaN   \n",
       "20140108      ...       4.525617  78.146377        NaN        NaN        NaN   \n",
       "\n",
       "symbol      603799.SH  603833.SH  603858.SH  603885.SH  603993.SH  \n",
       "trade_date                                                         \n",
       "20140102          NaN        NaN        NaN        NaN   6.630090  \n",
       "20140103          NaN        NaN        NaN        NaN   6.604232  \n",
       "20140106          NaN        NaN        NaN        NaN   6.546882  \n",
       "20140107          NaN        NaN        NaN        NaN   6.481907  \n",
       "20140108          NaN        NaN        NaN        NaN   6.410478  \n",
       "\n",
       "[5 rows x 488 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dv.add_formula(\"double_SMA\",\"SMA(SMA(close_adj,3,1),3,1)\",\n",
    "               is_quarterly=False,\n",
    "               add_data=True,\n",
    "               register_funcs={\"SMA\":sma}).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 方法2: append_df 构造一个因子表格（pandas.Dataframe）,直接添加到dataview当中\n",
    "更高自由度，只需要任意事先计算出一个因子表格，均可以添加到数据集里"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>symbol</th>\n",
       "      <th>000001.SZ</th>\n",
       "      <th>000002.SZ</th>\n",
       "      <th>000008.SZ</th>\n",
       "      <th>000009.SZ</th>\n",
       "      <th>000012.SZ</th>\n",
       "      <th>000024.SZ</th>\n",
       "      <th>000027.SZ</th>\n",
       "      <th>000039.SZ</th>\n",
       "      <th>000046.SZ</th>\n",
       "      <th>000059.SZ</th>\n",
       "      <th>...</th>\n",
       "      <th>601998.SH</th>\n",
       "      <th>603000.SH</th>\n",
       "      <th>603160.SH</th>\n",
       "      <th>603288.SH</th>\n",
       "      <th>603699.SH</th>\n",
       "      <th>603799.SH</th>\n",
       "      <th>603833.SH</th>\n",
       "      <th>603858.SH</th>\n",
       "      <th>603885.SH</th>\n",
       "      <th>603993.SH</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20171225</th>\n",
       "      <td>-5.964987</td>\n",
       "      <td>-2.935803</td>\n",
       "      <td>-0.667269</td>\n",
       "      <td>0.272287</td>\n",
       "      <td>2.049269</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.072746</td>\n",
       "      <td>-4.823338</td>\n",
       "      <td>0.141595</td>\n",
       "      <td>0.071706</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007734</td>\n",
       "      <td>0.495512</td>\n",
       "      <td>1.065298</td>\n",
       "      <td>-1.027654</td>\n",
       "      <td>0.013481</td>\n",
       "      <td>1.026721</td>\n",
       "      <td>-0.610970</td>\n",
       "      <td>0.146825</td>\n",
       "      <td>-0.167882</td>\n",
       "      <td>0.164181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20171226</th>\n",
       "      <td>-7.955376</td>\n",
       "      <td>-10.674239</td>\n",
       "      <td>-1.047420</td>\n",
       "      <td>0.209359</td>\n",
       "      <td>1.611634</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.038910</td>\n",
       "      <td>-4.209824</td>\n",
       "      <td>0.223873</td>\n",
       "      <td>0.054990</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000740</td>\n",
       "      <td>0.522930</td>\n",
       "      <td>1.105218</td>\n",
       "      <td>-0.566065</td>\n",
       "      <td>-0.006355</td>\n",
       "      <td>0.692044</td>\n",
       "      <td>-0.359273</td>\n",
       "      <td>0.103882</td>\n",
       "      <td>-0.093462</td>\n",
       "      <td>0.140671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20171227</th>\n",
       "      <td>-7.844112</td>\n",
       "      <td>-22.146334</td>\n",
       "      <td>-1.124412</td>\n",
       "      <td>0.103469</td>\n",
       "      <td>1.035090</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.005921</td>\n",
       "      <td>-3.235674</td>\n",
       "      <td>0.327199</td>\n",
       "      <td>0.029917</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008968</td>\n",
       "      <td>0.560068</td>\n",
       "      <td>1.066272</td>\n",
       "      <td>-0.342089</td>\n",
       "      <td>-0.027669</td>\n",
       "      <td>0.425376</td>\n",
       "      <td>-0.055576</td>\n",
       "      <td>0.093022</td>\n",
       "      <td>-0.084378</td>\n",
       "      <td>0.096784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20171228</th>\n",
       "      <td>-6.206059</td>\n",
       "      <td>-33.122673</td>\n",
       "      <td>-0.997696</td>\n",
       "      <td>0.082896</td>\n",
       "      <td>0.316651</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.056674</td>\n",
       "      <td>-2.627907</td>\n",
       "      <td>0.543418</td>\n",
       "      <td>0.006268</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009462</td>\n",
       "      <td>0.543119</td>\n",
       "      <td>0.976696</td>\n",
       "      <td>-0.213343</td>\n",
       "      <td>-0.036400</td>\n",
       "      <td>0.043794</td>\n",
       "      <td>0.179273</td>\n",
       "      <td>0.109964</td>\n",
       "      <td>-0.121064</td>\n",
       "      <td>-0.049372</td>\n",
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       "    <tr>\n",
       "      <th>20171229</th>\n",
       "      <td>-2.905227</td>\n",
       "      <td>-37.173927</td>\n",
       "      <td>-0.941555</td>\n",
       "      <td>0.005446</td>\n",
       "      <td>-0.280804</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.025376</td>\n",
       "      <td>-2.701184</td>\n",
       "      <td>0.562552</td>\n",
       "      <td>-0.012252</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006006</td>\n",
       "      <td>0.489031</td>\n",
       "      <td>0.747401</td>\n",
       "      <td>-0.088527</td>\n",
       "      <td>-0.049817</td>\n",
       "      <td>-0.114670</td>\n",
       "      <td>0.361152</td>\n",
       "      <td>0.099849</td>\n",
       "      <td>-0.204568</td>\n",
       "      <td>-0.122450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 488 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "symbol      000001.SZ  000002.SZ  000008.SZ  000009.SZ  000012.SZ  000024.SZ  \\\n",
       "trade_date                                                                     \n",
       "20171225    -5.964987  -2.935803  -0.667269   0.272287   2.049269        NaN   \n",
       "20171226    -7.955376 -10.674239  -1.047420   0.209359   1.611634        NaN   \n",
       "20171227    -7.844112 -22.146334  -1.124412   0.103469   1.035090        NaN   \n",
       "20171228    -6.206059 -33.122673  -0.997696   0.082896   0.316651        NaN   \n",
       "20171229    -2.905227 -37.173927  -0.941555   0.005446  -0.280804        NaN   \n",
       "\n",
       "symbol      000027.SZ  000039.SZ  000046.SZ  000059.SZ    ...      601998.SH  \\\n",
       "trade_date                                                ...                  \n",
       "20171225    -0.072746  -4.823338   0.141595   0.071706    ...       0.007734   \n",
       "20171226    -0.038910  -4.209824   0.223873   0.054990    ...      -0.000740   \n",
       "20171227     0.005921  -3.235674   0.327199   0.029917    ...      -0.008968   \n",
       "20171228     0.056674  -2.627907   0.543418   0.006268    ...      -0.009462   \n",
       "20171229     0.025376  -2.701184   0.562552  -0.012252    ...      -0.006006   \n",
       "\n",
       "symbol      603000.SH  603160.SH  603288.SH  603699.SH  603799.SH  603833.SH  \\\n",
       "trade_date                                                                     \n",
       "20171225     0.495512   1.065298  -1.027654   0.013481   1.026721  -0.610970   \n",
       "20171226     0.522930   1.105218  -0.566065  -0.006355   0.692044  -0.359273   \n",
       "20171227     0.560068   1.066272  -0.342089  -0.027669   0.425376  -0.055576   \n",
       "20171228     0.543119   0.976696  -0.213343  -0.036400   0.043794   0.179273   \n",
       "20171229     0.489031   0.747401  -0.088527  -0.049817  -0.114670   0.361152   \n",
       "\n",
       "symbol      603858.SH  603885.SH  603993.SH  \n",
       "trade_date                                   \n",
       "20171225     0.146825  -0.167882   0.164181  \n",
       "20171226     0.103882  -0.093462   0.140671  \n",
       "20171227     0.093022  -0.084378   0.096784  \n",
       "20171228     0.109964  -0.121064  -0.049372  \n",
       "20171229     0.099849  -0.204568  -0.122450  \n",
       "\n",
       "[5 rows x 488 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import talib as ta\n",
    "\n",
    "close = dv.get_ts(\"close_adj\").dropna(how='all', axis=1)\n",
    "slope_df = pd.DataFrame({sec_symbol: -ta.LINEARREG_SLOPE(value.values, 10) for sec_symbol, value in close.iteritems()}, index=close.index)\n",
    "dv.append_df(slope_df,'slope')\n",
    "dv.get_ts(\"slope\").tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2、什么是事件？\n",
    "\n",
    "事件是因子的一种特殊形式，用1/0/-1表示。\n",
    "比方说，我们可以定义长短期均线金叉为一个事件，股价走势发生金叉记为1，死叉记为-1，其他记位0。\n",
    "通过事件分析，可以测试某个事件和股票未来收益的关系。\n",
    "\n",
    "运用上面提供的自定义因子方法，构造一个5日/10日均线的金叉事件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from jaqs_fxdayu.research.signaldigger import process\n",
    "\n",
    "Open = dv.get_ts(\"open_adj\")\n",
    "High = dv.get_ts(\"high_adj\")\n",
    "Low = dv.get_ts(\"low_adj\")\n",
    "Close = dv.get_ts(\"close_adj\")\n",
    "trade_status = dv.get_ts('trade_status')\n",
    "mask_sus = trade_status == 0\n",
    "# 剔除掉停牌期的数据　再计算指标\n",
    "open_masked = process._mask_df(Open,mask=mask_sus)\n",
    "high_masked = process._mask_df(High,mask=mask_sus)\n",
    "low_masked = process._mask_df(Low,mask=mask_sus)\n",
    "close_masked = process._mask_df(Close,mask=mask_sus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Field [MA5] is overwritten.\n",
      "Field [MA10] is overwritten.\n"
     ]
    }
   ],
   "source": [
    "from jaqs_fxdayu.data import signal_function_mod as sfm\n",
    "MA5 = sfm.ta(ta_method='MA',\n",
    "             ta_column=0, \n",
    "             Open=open_masked, \n",
    "             High=high_masked, \n",
    "             Low=low_masked, \n",
    "             Close=close_masked,\n",
    "             Volume=None,\n",
    "             timeperiod=5)\n",
    "MA10 = sfm.ta('MA',Close=close_masked, timeperiod=10)\n",
    "dv.append_df(MA5,'MA5')\n",
    "dv.append_df(MA10,'MA10')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Field [Cross] is overwritten.\n"
     ]
    },
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       "    }\n",
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>symbol</th>\n",
       "      <th>000001.SZ</th>\n",
       "      <th>000002.SZ</th>\n",
       "      <th>000008.SZ</th>\n",
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       "      <th>...</th>\n",
       "      <th>601998.SH</th>\n",
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       "      <th>603288.SH</th>\n",
       "      <th>603699.SH</th>\n",
       "      <th>603799.SH</th>\n",
       "      <th>603833.SH</th>\n",
       "      <th>603858.SH</th>\n",
       "      <th>603885.SH</th>\n",
       "      <th>603993.SH</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20140102</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20140103</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20140106</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20140107</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20140108</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20140109</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20140110</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20140113</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20140114</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20140115</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20140116</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20140117</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20140120</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20140121</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20140122</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20140123</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20140124</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20140127</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20140128</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20140129</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20140130</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20140207</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20140210</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20140211</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20140212</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20140213</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20140214</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20140217</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20140218</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20140219</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20171120</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20171121</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20171122</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20171123</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20171124</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20171127</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20171128</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20171129</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20171130</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20171201</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20171204</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20171205</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20171206</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20171207</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20171208</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20171211</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20171212</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20171213</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20171214</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20171215</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20171218</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20171219</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20171220</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20171221</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20171222</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20171225</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20171226</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20171227</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20171228</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20171229</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>977 rows × 488 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "symbol      000001.SZ  000002.SZ  000008.SZ  000009.SZ  000012.SZ  000024.SZ  \\\n",
       "trade_date                                                                     \n",
       "20140102          NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "20140103          NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "20140106          NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "20140107          NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "20140108          NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "20140109          NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "20140110          NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "20140113          NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "20140114          NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "20140115          NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "20140116          0.0        0.0        1.0        0.0        0.0        0.0   \n",
       "20140117          0.0        0.0        0.0        0.0        1.0        0.0   \n",
       "20140120          0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "20140121          0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "20140122          0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "20140123          0.0        1.0        0.0        0.0        0.0        1.0   \n",
       "20140124          0.0        0.0        0.0        1.0        0.0        0.0   \n",
       "20140127          1.0        0.0        0.0        0.0        0.0        0.0   \n",
       "20140128          0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "20140129          0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "20140130          0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "20140207          0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "20140210          0.0        0.0        1.0        0.0        0.0        0.0   \n",
       "20140211          0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "20140212          1.0        0.0        0.0        0.0        0.0        0.0   \n",
       "20140213          0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "20140214          0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "20140217          0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "20140218          0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "20140219          0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "...               ...        ...        ...        ...        ...        ...   \n",
       "20171120          0.0        0.0        0.0        0.0        0.0        NaN   \n",
       "20171121          0.0        0.0        0.0        0.0        0.0        NaN   \n",
       "20171122          0.0        0.0        0.0        0.0        0.0        NaN   \n",
       "20171123          0.0        0.0        0.0        0.0        0.0        NaN   \n",
       "20171124          0.0        0.0        0.0        0.0        0.0        NaN   \n",
       "20171127          0.0        0.0        0.0        0.0        0.0        NaN   \n",
       "20171128          0.0        0.0        0.0        0.0        0.0        NaN   \n",
       "20171129          0.0        0.0        0.0        0.0        0.0        NaN   \n",
       "20171130          0.0        0.0        0.0        1.0        0.0        NaN   \n",
       "20171201          0.0        0.0        0.0        0.0        0.0        NaN   \n",
       "20171204          0.0        0.0        0.0        0.0        0.0        NaN   \n",
       "20171205          0.0        0.0        0.0        0.0        0.0        NaN   \n",
       "20171206          0.0        0.0        1.0        0.0        0.0        NaN   \n",
       "20171207          0.0        0.0        0.0        0.0        0.0        NaN   \n",
       "20171208          0.0        0.0        0.0        0.0        0.0        NaN   \n",
       "20171211          0.0        0.0        0.0        0.0        0.0        NaN   \n",
       "20171212          0.0        0.0        0.0        0.0        0.0        NaN   \n",
       "20171213          0.0        0.0        0.0        0.0        0.0        NaN   \n",
       "20171214          1.0        0.0        0.0        1.0        0.0        NaN   \n",
       "20171215          0.0        0.0        0.0        0.0        0.0        NaN   \n",
       "20171218          0.0        0.0        0.0        0.0        0.0        NaN   \n",
       "20171219          0.0        0.0        0.0        0.0        0.0        NaN   \n",
       "20171220          0.0        0.0        1.0        0.0        0.0        NaN   \n",
       "20171221          0.0        0.0        0.0        0.0        0.0        NaN   \n",
       "20171222          1.0        0.0        0.0        0.0        0.0        NaN   \n",
       "20171225          0.0        1.0        0.0        0.0        0.0        NaN   \n",
       "20171226          0.0        0.0        0.0        0.0        0.0        NaN   \n",
       "20171227          0.0        0.0        0.0        0.0        0.0        NaN   \n",
       "20171228          0.0        0.0        0.0        0.0        0.0        NaN   \n",
       "20171229          0.0        0.0        0.0        1.0        1.0        NaN   \n",
       "\n",
       "symbol      000027.SZ  000039.SZ  000046.SZ  000059.SZ    ...      601998.SH  \\\n",
       "trade_date                                                ...                  \n",
       "20140102          NaN        NaN        NaN        NaN    ...            NaN   \n",
       "20140103          NaN        NaN        NaN        NaN    ...            NaN   \n",
       "20140106          NaN        NaN        NaN        NaN    ...            NaN   \n",
       "20140107          NaN        NaN        NaN        NaN    ...            NaN   \n",
       "20140108          NaN        NaN        NaN        NaN    ...            NaN   \n",
       "20140109          NaN        NaN        NaN        NaN    ...            NaN   \n",
       "20140110          NaN        NaN        NaN        NaN    ...            NaN   \n",
       "20140113          NaN        NaN        NaN        NaN    ...            NaN   \n",
       "20140114          NaN        NaN        NaN        NaN    ...            NaN   \n",
       "20140115          NaN        NaN        NaN        NaN    ...            NaN   \n",
       "20140116          0.0        0.0        1.0        0.0    ...            0.0   \n",
       "20140117          1.0        0.0        0.0        0.0    ...            0.0   \n",
       "20140120          0.0        0.0        0.0        0.0    ...            0.0   \n",
       "20140121          0.0        0.0        0.0        0.0    ...            0.0   \n",
       "20140122          0.0        0.0        0.0        0.0    ...            0.0   \n",
       "20140123          0.0        0.0        1.0        0.0    ...            0.0   \n",
       "20140124          0.0        0.0        0.0        0.0    ...            0.0   \n",
       "20140127          0.0        0.0        0.0        0.0    ...            1.0   \n",
       "20140128          0.0        0.0        0.0        1.0    ...            0.0   \n",
       "20140129          0.0        0.0        0.0        0.0    ...            0.0   \n",
       "20140130          0.0        0.0        0.0        0.0    ...            0.0   \n",
       "20140207          0.0        0.0        0.0        0.0    ...            0.0   \n",
       "20140210          0.0        0.0        0.0        0.0    ...            0.0   \n",
       "20140211          0.0        0.0        0.0        0.0    ...            0.0   \n",
       "20140212          0.0        0.0        0.0        0.0    ...            0.0   \n",
       "20140213          0.0        0.0        0.0        0.0    ...            0.0   \n",
       "20140214          0.0        1.0        1.0        1.0    ...            0.0   \n",
       "20140217          0.0        0.0        0.0        0.0    ...            0.0   \n",
       "20140218          0.0        0.0        0.0        0.0    ...            0.0   \n",
       "20140219          0.0        0.0        0.0        0.0    ...            0.0   \n",
       "...               ...        ...        ...        ...    ...            ...   \n",
       "20171120          0.0        0.0        0.0        0.0    ...            0.0   \n",
       "20171121          0.0        0.0        0.0        0.0    ...            1.0   \n",
       "20171122          0.0        0.0        0.0        0.0    ...            0.0   \n",
       "20171123          0.0        0.0        0.0        0.0    ...            0.0   \n",
       "20171124          0.0        0.0        1.0        0.0    ...            0.0   \n",
       "20171127          0.0        0.0        0.0        0.0    ...            0.0   \n",
       "20171128          0.0        0.0        0.0        0.0    ...            0.0   \n",
       "20171129          0.0        0.0        0.0        0.0    ...            0.0   \n",
       "20171130          0.0        0.0        0.0        1.0    ...            0.0   \n",
       "20171201          0.0        0.0        0.0        0.0    ...            0.0   \n",
       "20171204          0.0        0.0        0.0        0.0    ...            0.0   \n",
       "20171205          0.0        1.0        0.0        0.0    ...            1.0   \n",
       "20171206          0.0        0.0        0.0        0.0    ...            0.0   \n",
       "20171207          0.0        0.0        0.0        0.0    ...            0.0   \n",
       "20171208          0.0        0.0        0.0        0.0    ...            0.0   \n",
       "20171211          0.0        0.0        0.0        0.0    ...            0.0   \n",
       "20171212          0.0        1.0        0.0        0.0    ...            0.0   \n",
       "20171213          0.0        0.0        1.0        0.0    ...            0.0   \n",
       "20171214          1.0        0.0        0.0        0.0    ...            0.0   \n",
       "20171215          0.0        0.0        0.0        0.0    ...            0.0   \n",
       "20171218          0.0        0.0        0.0        0.0    ...            0.0   \n",
       "20171219          0.0        0.0        0.0        0.0    ...            0.0   \n",
       "20171220          0.0        0.0        0.0        0.0    ...            0.0   \n",
       "20171221          0.0        0.0        0.0        0.0    ...            0.0   \n",
       "20171222          0.0        0.0        0.0        0.0    ...            0.0   \n",
       "20171225          0.0        0.0        0.0        0.0    ...            0.0   \n",
       "20171226          0.0        0.0        0.0        0.0    ...            1.0   \n",
       "20171227          0.0        0.0        0.0        0.0    ...            0.0   \n",
       "20171228          0.0        0.0        0.0        0.0    ...            0.0   \n",
       "20171229          0.0        0.0        0.0        0.0    ...            0.0   \n",
       "\n",
       "symbol      603000.SH  603160.SH  603288.SH  603699.SH  603799.SH  603833.SH  \\\n",
       "trade_date                                                                     \n",
       "20140102          NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "20140103          NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "20140106          NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "20140107          NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "20140108          NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "20140109          NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "20140110          NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "20140113          NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "20140114          NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "20140115          NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "20140116          0.0        NaN        NaN        NaN        NaN        NaN   \n",
       "20140117          0.0        NaN        NaN        NaN        NaN        NaN   \n",
       "20140120          0.0        NaN        NaN        NaN        NaN        NaN   \n",
       "20140121          0.0        NaN        NaN        NaN        NaN        NaN   \n",
       "20140122          0.0        NaN        NaN        NaN        NaN        NaN   \n",
       "20140123          0.0        NaN        NaN        NaN        NaN        NaN   \n",
       "20140124          0.0        NaN        NaN        NaN        NaN        NaN   \n",
       "20140127          0.0        NaN        NaN        NaN        NaN        NaN   \n",
       "20140128          0.0        NaN        NaN        NaN        NaN        NaN   \n",
       "20140129          0.0        NaN        NaN        NaN        NaN        NaN   \n",
       "20140130          0.0        NaN        NaN        NaN        NaN        NaN   \n",
       "20140207          0.0        NaN        NaN        0.0        NaN        NaN   \n",
       "20140210          0.0        NaN        NaN        0.0        NaN        NaN   \n",
       "20140211          0.0        NaN        NaN        0.0        NaN        NaN   \n",
       "20140212          0.0        NaN        NaN        0.0        NaN        NaN   \n",
       "20140213          0.0        NaN        NaN        0.0        NaN        NaN   \n",
       "20140214          0.0        NaN        NaN        1.0        NaN        NaN   \n",
       "20140217          0.0        NaN        NaN        0.0        NaN        NaN   \n",
       "20140218          0.0        NaN        NaN        0.0        NaN        NaN   \n",
       "20140219          0.0        NaN        NaN        0.0        NaN        NaN   \n",
       "...               ...        ...        ...        ...        ...        ...   \n",
       "20171120          0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "20171121          0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "20171122          0.0        0.0        1.0        0.0        0.0        0.0   \n",
       "20171123          0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "20171124          0.0        1.0        1.0        0.0        1.0        0.0   \n",
       "20171127          0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "20171128          0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "20171129          0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "20171130          0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "20171201          0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "20171204          0.0        0.0        0.0        0.0        1.0        0.0   \n",
       "20171205          0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "20171206          0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "20171207          0.0        0.0        0.0        1.0        0.0        0.0   \n",
       "20171208          0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "20171211          0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "20171212          0.0        0.0        1.0        0.0        0.0        1.0   \n",
       "20171213          0.0        0.0        0.0        0.0        1.0        0.0   \n",
       "20171214          0.0        1.0        0.0        0.0        0.0        0.0   \n",
       "20171215          1.0        0.0        0.0        0.0        0.0        0.0   \n",
       "20171218          0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "20171219          0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "20171220          0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "20171221          0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "20171222          0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "20171225          0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "20171226          0.0        0.0        0.0        1.0        0.0        0.0   \n",
       "20171227          0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "20171228          0.0        0.0        0.0        0.0        1.0        0.0   \n",
       "20171229          0.0        0.0        0.0        0.0        0.0        0.0   \n",
       "\n",
       "symbol      603858.SH  603885.SH  603993.SH  \n",
       "trade_date                                   \n",
       "20140102          NaN        NaN        NaN  \n",
       "20140103          NaN        NaN        NaN  \n",
       "20140106          NaN        NaN        NaN  \n",
       "20140107          NaN        NaN        NaN  \n",
       "20140108          NaN        NaN        NaN  \n",
       "20140109          NaN        NaN        NaN  \n",
       "20140110          NaN        NaN        NaN  \n",
       "20140113          NaN        NaN        NaN  \n",
       "20140114          NaN        NaN        NaN  \n",
       "20140115          NaN        NaN        NaN  \n",
       "20140116          NaN        NaN        0.0  \n",
       "20140117          NaN        NaN        1.0  \n",
       "20140120          NaN        NaN        0.0  \n",
       "20140121          NaN        NaN        0.0  \n",
       "20140122          NaN        NaN        1.0  \n",
       "20140123          NaN        NaN        0.0  \n",
       "20140124          NaN        NaN        0.0  \n",
       "20140127          NaN        NaN        1.0  \n",
       "20140128          NaN        NaN        0.0  \n",
       "20140129          NaN        NaN        0.0  \n",
       "20140130          NaN        NaN        0.0  \n",
       "20140207          NaN        NaN        0.0  \n",
       "20140210          NaN        NaN        0.0  \n",
       "20140211          NaN        NaN        0.0  \n",
       "20140212          NaN        NaN        0.0  \n",
       "20140213          NaN        NaN        0.0  \n",
       "20140214          NaN        NaN        0.0  \n",
       "20140217          NaN        NaN        0.0  \n",
       "20140218          NaN        NaN        0.0  \n",
       "20140219          NaN        NaN        0.0  \n",
       "...               ...        ...        ...  \n",
       "20171120          0.0        0.0        0.0  \n",
       "20171121          0.0        0.0        0.0  \n",
       "20171122          0.0        0.0        0.0  \n",
       "20171123          0.0        0.0        0.0  \n",
       "20171124          0.0        0.0        0.0  \n",
       "20171127          0.0        0.0        1.0  \n",
       "20171128          0.0        0.0        0.0  \n",
       "20171129          0.0        0.0        0.0  \n",
       "20171130          0.0        0.0        0.0  \n",
       "20171201          0.0        0.0        0.0  \n",
       "20171204          0.0        0.0        0.0  \n",
       "20171205          0.0        0.0        0.0  \n",
       "20171206          0.0        0.0        0.0  \n",
       "20171207          0.0        0.0        0.0  \n",
       "20171208          0.0        0.0        0.0  \n",
       "20171211          0.0        1.0        0.0  \n",
       "20171212          0.0        0.0        0.0  \n",
       "20171213          0.0        0.0        0.0  \n",
       "20171214          0.0        0.0        1.0  \n",
       "20171215          0.0        0.0        0.0  \n",
       "20171218          0.0        0.0        0.0  \n",
       "20171219          0.0        0.0        0.0  \n",
       "20171220          0.0        0.0        0.0  \n",
       "20171221          0.0        0.0        0.0  \n",
       "20171222          0.0        0.0        0.0  \n",
       "20171225          0.0        0.0        0.0  \n",
       "20171226          0.0        0.0        0.0  \n",
       "20171227          0.0        0.0        0.0  \n",
       "20171228          0.0        0.0        1.0  \n",
       "20171229          0.0        0.0        0.0  \n",
       "\n",
       "[977 rows x 488 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 定义金叉事件\n",
    "dv.add_formula(\"Cross\",\"(MA5>=MA10)&&(Delay(MA5<MA10, 1))\",is_quarterly=False, add_data=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "事件分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Field [up_limit] is overwritten.\n",
      "Field [down_limit] is overwritten.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "#定义信号过滤条件-非指数成分\n",
    "def mask_index_member():\n",
    "    df_index_member = dv.get_ts('index_member')\n",
    "    mask_index_member = df_index_member == 0\n",
    "    return mask_index_member\n",
    "\n",
    "# 定义可买卖条件——未停牌、未涨跌停\n",
    "def limit_up_down():\n",
    "    trade_status = dv.get_ts('trade_status')\n",
    "    mask_sus = trade_status == 0\n",
    "    # 涨停\n",
    "    dv.add_formula('up_limit', '(close - Delay(close, 1)) / Delay(close, 1) > 0.095', is_quarterly=False, add_data=True)\n",
    "    # 跌停\n",
    "    dv.add_formula('down_limit', '(close - Delay(close, 1)) / Delay(close, 1) < -0.095', is_quarterly=False, add_data=True)\n",
    "    can_enter = np.logical_and(dv.get_ts('up_limit') < 1, ~mask_sus) # 未涨停未停牌\n",
    "    can_exit = np.logical_and(dv.get_ts('down_limit') < 1, ~mask_sus) # 未跌停未停牌\n",
    "    return can_enter,can_exit\n",
    "\n",
    "mask = mask_index_member()\n",
    "can_enter,can_exit = limit_up_down()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nan Data Count (should be zero) : 0;  Percentage of effective data: 56%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>signal</th>\n",
       "      <th>return</th>\n",
       "      <th>upside_ret</th>\n",
       "      <th>downside_ret</th>\n",
       "      <th>quantile</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <th>symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">20140117</th>\n",
       "      <th>000001.SZ</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.051465</td>\n",
       "      <td>0.088050</td>\n",
       "      <td>-0.033901</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000002.SZ</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.040160</td>\n",
       "      <td>0.119256</td>\n",
       "      <td>-0.006450</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000009.SZ</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.264483</td>\n",
       "      <td>0.322153</td>\n",
       "      <td>-0.012334</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000012.SZ</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.097084</td>\n",
       "      <td>0.130152</td>\n",
       "      <td>-0.008737</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000024.SZ</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.064774</td>\n",
       "      <td>0.155523</td>\n",
       "      <td>-0.008997</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      signal    return  upside_ret  downside_ret  quantile\n",
       "trade_date symbol                                                         \n",
       "20140117   000001.SZ     0.0  0.051465    0.088050     -0.033901         1\n",
       "           000002.SZ     0.0  0.040160    0.119256     -0.006450         1\n",
       "           000009.SZ     0.0  0.264483    0.322153     -0.012334         1\n",
       "           000012.SZ     0.0  0.097084    0.130152     -0.008737         1\n",
       "           000024.SZ     0.0  0.064774    0.155523     -0.008997         1"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from jaqs_fxdayu.research import SignalDigger\n",
    "obj = SignalDigger(output_folder='./output',\n",
    "                   output_format='pdf')\n",
    "\n",
    "# 和处理因子的步骤一样 n_quantiles=1\n",
    "# 不传入benchmark 可以分析绝对收益\n",
    "obj.process_signal_before_analysis(signal=dv.get_ts(\"Cross\"),\n",
    "                                   price=dv.get_ts(\"close_adj\"),\n",
    "                                   high=dv.get_ts(\"high_adj\"), # 可为空\n",
    "                                   low=dv.get_ts(\"low_adj\"),# 可为空\n",
    "                                   n_quantiles=1,# quantile分类数\n",
    "                                   mask=mask,# 过滤条件\n",
    "                                   can_enter=can_enter,# 是否能进场\n",
    "                                   can_exit=can_exit,# 是否能出场\n",
    "                                   period=15,# 持有期\n",
    "                                   # benchmark_price=dv.data_benchmark, # 基准价格 可不传入，持有期收益（return）计算为绝对收益\n",
    "                                   commission = 0.0008,\n",
    "                                   )\n",
    "signal_data = obj.signal_data\n",
    "signal_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "——选股收益分析——\n",
      "               long_ret  long_short_ret  all_sample_ret\n",
      "t-stat        12.598541       -2.532330       53.562672\n",
      "p-value        0.000000        0.011500        0.000000\n",
      "skewness       0.687618        0.384795        0.541517\n",
      "kurtosis       5.757444        6.439517        6.210530\n",
      "Ann. Ret       0.172894       -0.029187        0.181235\n",
      "Ann. Vol       0.422764        0.085846        0.434142\n",
      "Ann. IR        0.408960       -0.339994        0.417456\n",
      "occurance  15312.000000      896.000000   265600.000000\n",
      "——最大潜在盈利/亏损分析——\n",
      "                 long_space  all_sample_space\n",
      "Up_sp Mean         0.082264          0.084310\n",
      "Up_sp Std          0.089366          0.092245\n",
      "Up_sp IR           0.920522          0.913982\n",
      "Up_sp Pct5         0.001019          0.001947\n",
      "Up_sp Pct25        0.022871          0.023772\n",
      "Up_sp Pct50        0.056777          0.057538\n",
      "Up_sp Pct75        0.111500          0.113509\n",
      "Up_sp Pct95        0.250879          0.257973\n",
      "Up_sp Occur    15312.000000     265600.000000\n",
      "Down_sp Mean      -0.098562         -0.099719\n",
      "Down_sp Std        0.187441          0.190518\n",
      "Down_sp IR        -0.525831         -0.523410\n",
      "Down_sp Pct5      -0.330871         -0.347779\n",
      "Down_sp Pct25     -0.092257         -0.091053\n",
      "Down_sp Pct50     -0.046429         -0.045785\n",
      "Down_sp Pct75     -0.021270         -0.020408\n",
      "Down_sp Pct95     -0.004611         -0.003867\n",
      "Down_sp Occur  15312.000000     265600.000000\n"
     ]
    }
   ],
   "source": [
    "from jaqs_fxdayu.research.signaldigger.analysis import analysis\n",
    "result = analysis(signal_data, is_event=True, period=15)\n",
    "print(\"——选股收益分析——\")\n",
    "print(result[\"ret\"])\n",
    "print(\"——最大潜在盈利/亏损分析——\")\n",
    "print(result[\"space\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Value of signals of Different Quantiles Statistics\n",
      "          min  max      mean       std   count  count %\n",
      "quantile                                               \n",
      "1         0.0  1.0  0.057651  0.233082  265600    100.0\n",
      "Figure saved: E:\\2018_Course\\HighSchool\\Final\\5_因子研发工具实操Richard\\output\\returns_report.pdf\n",
      "Information Analysis\n",
      "                ic\n",
      "IC Mean     -0.005\n",
      "IC Std.      0.084\n",
      "t-stat(IC)  -1.615\n",
      "p-value(IC)  0.107\n",
      "IC Skew     -0.217\n",
      "IC Kurtosis  1.232\n",
      "Ann. IR     -0.054\n",
      "Figure saved: E:\\2018_Course\\HighSchool\\Final\\5_因子研发工具实操Richard\\output\\information_report.pdf\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d0b7982860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Dy5YtS/Mdus2bNyswMFBVq1aVh4eH2rRpo2+//VYJCQnJ+iYmJmru3Llq1aqV\nPD091bhxY82dO1dJSUlPcMbSf6x79+7Vhg0b1L59e3l6esrPz09DhgzR+fPnUxx/2bJlateunby9\nvVW7dm2NGzdOt27dUqVKlRQYGCjp/94vk6SYmBhZrVY1aNAg2Vj79u1Tt27d5O3tLV9fX/Xv318R\nERGPdJy2+h/2n62mh5k4caK2bNkiPz8/LV++PM2gdv78ed28eVMVK1Z8pLHTEhsbq6CgIDVp0kQe\nHh5q1aqVVq9enWr/27dva+bMmWrfvr2qVasmd3d31alTR0OGDNHvv/9u77d+/XpZrVYNGDAgxXE2\nbNggq9Xq8Mjktm3b1KNHD9WqVUseHh5q2rSpJk6cmOJ7hM7OzmrdurVCQ0N19OjRJzgDAPBkeOQS\nAJ4xcXFx2rRpkywWi0qUKJFin4ULF2rHjh2yWCyqX7++wsLCFBQUpH379mnOnDkOiz5PmTJFM2fO\nVM6cOeXh4aE8efIoNDRU//nPfxQeHq4JEyYkG/+zzz7T1atXVadOHUVERMhqtaYaFidMmKB58+Yp\ne/bsql69unLlyqUDBw7o008/1bZt2zR79mzlzJlT0r1HEN955x1t2rRJefPmVZ06dXTt2jVNnDhR\n5cuXf+Jzl55jXbBggX766SdZrVbVrVtXhw4d0rp167R//35t2LBBL774or3vqFGjtHjxYrm4uOiV\nV17R7du3tWjRIoWHhzsExpIlS8rf319r165VtmzZ1Lx5c7m6ujrsd/fu3fr+++9VsmRJ1apVS8eP\nH9f27dsVGhqqtWvXqlixYmkeq9Vqlb+//0PPSbly5R7aR5IqVKigNm3aqGnTpg/ta3vcMn/+/Bo5\ncqT27NmjK1euqFSpUurQoYO6desmJ6eH/5txfHy8evXqpfDwcBUqVEj169fXhQsXNGzYsBSvh9u3\nb6tTp06KiIhQsWLF5Ofnp/j4eP36669at26dtm/fbj93jRo10osvvqjg4GBFR0cnC6hr1qyRdO9O\nmyStWrVKw4cPV44cOVStWjXlzp1bv/zyi+bOnavt27frhx9+SPY+ZP369TVnzhytXr1aHh4eDz1e\nAMgQBgDgmbJ7927DYrEYH3/8cbJtX375pWGxWAyLxWIEBQXZ22/evGl06tTJsFgsxqxZs+ztO3fu\nNCwWi9G0aVPj3Llz9vZr164Z7dq1MywWi/HDDz8kG79y5crGqVOnDMMwjMTERMMwDCMkJMSwWCxG\n37597f3iPy5LAAAgAElEQVQ3bdpkWCwWo379+sbp06ft7X///bfRrVs3w2KxGOPHj7e3r1271rBY\nLEabNm2M69ev29s3b95sVKhQwbBYLMawYcMeeo5WrFhhWCwWY/To0U98rFar1Vi1apW9/datW0ab\nNm0Mi8ViLFy40N6+a9cuw2KxGI0bNzYuXbpkbz9w4IDh5eVlWCwWo1u3bg51WiwWw8vLK8XaLRaL\nMXXqVCMpKckwDMOIj4+3n7Np06Y99BxktFdffdWwWCzGtWvXkm2bNm2a/RheeeUVY8CAAUZAQIDh\n7u5uWCwWY9CgQfbjSsvMmTMNi8Vi9OrVy4iNjbW3z5s3zz7+l19+aW+fMWOG/RqxXZeGYRi3b982\nAgMDDYvFYnz99df29o8++siwWCzGokWLHPZ77do1o1KlSkbLli3tbQ0bNjQqVapkv+4NwzASEhKM\nXr16GRaLxVi2bFmy+uPj441KlSoZTZs2feixAkBG4ZFLAHjGHDhwQJJksVhS7ePh4aGBAwfav86T\nJ48mTJigLFmyaOnSpfb2uXPnSpI+/vhjh1kHXV1dNW7cOEnSvHnzko1fq1YtlS1bVpLSvNMyf/58\nSdJHH32kMmXK2NtfeOEFffHFF8qePbuWLFmimJgYSdKSJUskSSNGjFD+/Pnt/Rs3bqw2bdqkup9H\nkd5jrVGjhv0ujSTlzp3b/vWpU6fs7d99950kaeTIkQ7vEVarVi1dSzmUKFFCb7/9trJkySJJyp49\nu7p06SJJj/zYZWax3aFr3bq1duzYoa+//lqLFy/WypUrVaxYMW3cuNHhOkzN0qVL5eTkpLFjx9rv\n4kpS9+7d5evrm6x/rly5VLduXb3zzjsO16WLi4v9buWlS5fs7e3atZMkrV271mGcDRs26O7duw7f\n96ioKGXLlk0FCxa0tzk7O2vYsGEaO3asvL29k9WTPXt2lSpVSmfOnNHVq1cferwAkBEIdADwjLGt\nOVe0aNFU+zRv3jxZW5kyZVSmTBmdO3dOkZGRSkxM1MGDB5UtWzb5+Pgk61+xYkUVKFBAx44d0+3b\ntx22pRUmbe7evavDhw8rZ86cqlu3brLthQoVUvXq1RUXF6eff/5ZSUlJOnTokFxcXFKsp2HDhg/d\nZ2qe5FhTelSuUKFCkmQPooZhaP/+/XJxcVHNmjWT9X+UxxQf5OnpmSwsv/TSS5KkmzdvPvZ4/6Qp\nU6Zo/fr1mjBhgnLkyGFvf/nllzVixAhJ9x4LTsvly5d14cIFlS1bNsVrPaXroUePHpo1a5bc3Nzs\nbdHR0QoJCVFoaKgkOczO6eXlpbJlyyo8PFwXLlywt//www/KmjWrwyOr1apVU1xcnF5//XXNmDFD\nx48fl3Tvz0LHjh1TfXTV9mjss7BWJIDnE+/QAcAzxjYBQ548eVLtk9oaXy+99JJOnz6tyMhIZcuW\nTXFxcZIkd3f3NPd55coVh1kN739vLDXR0dG6c+eOihYtqmzZUv7rxPbL7tWrV+39ixUrZr8rlVLf\n9IiOjk73sb7wwgvJ+mTNmlXS/y07EB0drdjYWJUuXTrFO5bpqT2t/T7KBDFBQUGaPn36Q/v5+vpq\nwYIFj11fWnLkyJHqO4916tRRtmzZFBERobt376Z6bURFRUmSQzi7X2rnNDIyUt99951CQ0N1+vRp\n/f3335Jkv6aMBya/adu2rT7//HOtW7dO/fv319mzZ3X06FHVrl3bYd9jxozRW2+9pZMnT2rq1Kma\nOnWqChcurEaNGqlr166pHq/t+3jt2rUUtwNARiPQAcAz5u7du5LS/qXe9ov/g2yfyZo1qxITEyXd\ne4QwpRkW73f/JCpS2o9Z2jz4i3Na9Tw4fkpSO6ZH8STHmlK4fJDte5LaMT/KuXjQo5zjtDztSVGe\nFmdnZ7344ou6fv264uLiUv2HiYed95Suh3379ql///6Ki4tT8eLFVbNmTZUrV05VqlTRX3/9pQ8+\n+CDZZ9q0aaMpU6bYA53t8cv7H7eUpOLFi2v16tXat2+ftm3bpj179ujs2bNatGiRli5dqi+++CLF\nO7G2a8/2vwDwTyPQAcAzJm/evJKkv/76K9U+kZGRKbb/+eefku7dqcuTJ4+cnZ1lGIYmT5781OvM\nly+fnJ2dFRkZmeqdGNtjbq6ursqfP7+yZ89ufxz0wV/Yr1y58sS1ZNSx5s+fXzly5FBkZKSSkpKS\nhbHMeNyuSZMmatKkyT++35iYGE2YMEF///23pk6dmuxcxMTE6Pr168qTJ0+ad5kLFy4sSbp48WKK\n2x+8HgzD0EcffaS4uDhNnjw5WZhN7Z09Nzc31apVS8HBwTp79qy2bNmi3Llzq3Hjxsn6Ojk5qVat\nWqpVq5ake9fvN998oyVLlqQa6Gx31G1/bgHgn8Y7dADwjLE9TplWwAkODk7W9ttvv+ncuXOqWLGi\nXF1dlT17dlWpUkUxMTEKCwtL1j8yMlJNmzbVm2++meJ6cQ/j7OwsT09PxcXFadeuXcm2X716VQcP\nHpSLi4sqV66sLFmyyNfXV7Gxsdq9e3ey/jt37nzsGmwy+lizZcumqlWrKi4uTiEhIcm2b9++PV11\nm5GLi4u2bdumTZs2KTw8PNl22x0wWyhKjZubm8qUKaOzZ886TD5j8+D1cP36dZ0/f17FihVL8c6k\n7ZpK6c62bXKU+fPn6+TJk2ratKnDJCwXL15U69at1bdvX4fPFS9eXB999JGcnJxSDe22P6elS5dO\n42gBIOMQ6ADgGePl5SVJOnLkSKp9tm7dqnXr1tm/vnHjhj788ENJ92YItLEtKj1ixAidOXPG3h4b\nG6v//Oc/Onv2rP3OWXrYxh87dqzOnj1rb79165bee+893blzR+3atbP/8myrbezYsfa7idK9R+m+\n//77dNXwYC0ZdazdunWTdK922/tfknT8+HHNmDEjxc/kyJFD8fHx6QqRz7LXX39dkjR69GiH2R2P\nHz+uKVOmyMnJSW+++eZDx7F9z4YPH64bN27Y21evXq1t27Y59M2XL59y5sypyMhI+yyb0r0AN3v2\nbG3atEnSvbXtHmRbk852jbVt29Zhe9GiRXXjxg0FBwfrp59+cti2YcMGJSUlqUqVKsnGvXHjhs6e\nPauSJUuqQIECDz1eAMgIPHIJAM8YPz8/ubi4pHinyaZIkSIaMmSIvvvuOxUqVEihoaGKjo5Wy5Yt\nHX5ZbdGihfbv36/FixerdevW8vDwUN68eXXo0CFdv35d5cuXtwfB9GjWrJm6deum7777Tv7+/vL1\n9bUvLB4dHa1q1arpvffes/evV6+eAgMDtWDBArVs2dK+OPeBAwdUpUqVNEPsw2T0sTZq1Eivvfaa\nVq9erWbNmsnPz08JCQnav3+/XnrpJUVHR8vZ2dnhM6VKlVJERIQ6deqkcuXK6bPPPkv3/p8lb731\nlg4cOKBDhw6pWbNm8vHxUUJCgkJDQ3X37l199NFHKQagB3Xu3Fl79+7V1q1b1aRJE/n6+ioqKkqH\nDx+Wp6enw/WQNWtWde3aVbNnz9brr78uPz8/5cyZU7/88osuX76s8uXL6/fff09x+YDs2bOrRYsW\nWrx4sYoVK6bq1as7bM+SJYv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p2bdvnzp16iQvLy81atRIy5cv18iRIx/pfeL/hZ9FTZo0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ASj98fvDBB/jggw+cHiM9Pb3Sx5kxYwbatWsHi8WCffv2YeXKlejRowcWLlyoBnMA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\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BkszRMaAoeDKLPxjnrvqWoKbNCWnR6qz3U1WVgq2/UvDbVpz79+HJztJecx74i6yFn2GO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\nH7dwdHSkRo0aXLt2zaZvVu7IPtLcfGckFvbnTmJ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WlFdyrfXguu9a6PI6duwYiYmJ6sXj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AAAgPPHGToAAAAA8FA0dAAAAADgoWjoAAAAAMBD0dABAAAAgIeioQMAAAAAD0VDBwAAAAAe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\nD/M0ViY2m03h4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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d0b40c5e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DQkICMjMzhWNd0fUAlF7HCQkJSE9Pl6p7VXMXCSGkNlAQRgj54n1IUFKdm9SSkhIAkOmB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GLFCvzwhz9EV1cXPvShDwEALr30Utxxxx24+uqr8dnPfhZdXV247777sHHjRvzHf/wHEokETj/9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\nVDRYxEgYwCJ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YB3JykYvuqamC5YEt2/iRj1yOhQs7LQ/VbduW+U4MPT0dcwtj8fuOH5/E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FgcrleepUuXQhAELFy4EP7+/njzzTfx1VdficXPi9jY2FSq3T+XxLe0tISlpSUePnz43P0S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\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ONKhfj3nzF9KzezdefuNt3B43jzz8IOFhYbyX+DEul4uEp5/i8NGjzPnuB6wWK/fe3Z23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\nEEIIoUH/B/BoSd7nj4MTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d0b5d0b860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化\n",
    "import matplotlib.pyplot as plt\n",
    "obj.create_full_report()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
